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Real-Estate Agent Commission Structure and Sales
Pieter GautierArjen SiegmannAico van Vuuren
This version: November 2017
Do higher real-estate agent fees imply better performance? This study uses a
nation-wide data set of residential real-estate transactions in the Netherlands
from 1985 to 2011 to provide evidence against this. Brokers with a flat-fee
structure who charge an up-front fee (which is substantially lower than the
average fee of traditional brokers) and leave the viewings to the seller sell
faster and at – on average – 2.7 percent higher prices. We correct for fixed
house- and time effects. We provide additional evidence that sellers who chose
for a flat fee broker were the ones who benefitted most from them.
Keywords: real-estate brokers, broker incentives, housing, agency
JEL-Classification: D80, L10, L80, R20, R30
Vrije Universiteit Amsterdam.
Vrije Universiteit Amsterdam, Corresponding author,
University of Gothenburg.
The authors thank Johan Walden, seminar participants at the VU and the European Finance As-
sociation 2016 for useful comments and suggestions. We thank NVM, the “Nederlandse Vereniging
van Makelaars o.g. en vastgoeddeskundigen” for providing the data.
1 Introduction
The majority of residential home sales is realized through the help of a real-estate
agent.1This is not surprising because both buying and selling a home involve
decisions that can have a large and long-lasting financial impact, and consumers
are typically not well informed about the real-estate market. However, real-estate
agents are expensive: a typical real-estate agent who represents the seller charges 6
percent of the sales price in the US, while the fee is between 2 and 3 percent in the
UK and about 2 percent in the Netherlands.2According to White (2007), 61 billion
dollars were spent on real-estate transaction fees in 2004 in the US.3Whether or
not those fees are excessive is an empirical question that we address in this paper.
If real-estate agents are very good in bringing (heterogeneous) sellers and buyers
together, they create surplus that could in principle justify high fees. So in order to
create surplus, real-estate agents should sell faster and/or at a higher price.
This paper addresses the performance of real-estate agents using a unique
case study for the Netherlands. In contrast to the US, almost all of the residential
property for sale is listed publicly in the Netherlands (as of 2001) on an internet
site called Funda. Originally, only traditional full-service brokers posted the houses
of their clients on this website and charged a fee of around 2 percent, payable after
the transaction was made (the average transaction price was almost 200,000 Euro).
In 2005, flat-fee brokers entered the market. These brokers charge an up-front fee,
1According to, 89 percent of home sellers in the United States use a real-estate agent,
while this number is 87 percent for home buyers.
2Note that in the US, the 6 percent also includes the fee that the real-estate agent of the seller
needs to pay to the real-estate agent of the buyer.
3Based on the data provided on, we obtain a figure of 70 billion dollars for 2015. This
conjecture is fed by the fact that most countries have a brokerage fee which is a fixed percentage
of the sales price. See OECD (2007) for a complete list of brokerage fees.
in the range of 400 to 1300 Euro, which is only a fraction of the average fee of the
traditional brokers. In addition to charging a flat fee, these brokers use the same
online multiple listing service as the traditional brokers. Moreover, they offer limited
additional services, such as price negotiation. The main difference with traditional
brokers is that they leave the viewings of the house to the seller.
We find the flat-fee strategy to be the only broker characteristic that is sig-
nificant in explaining differences in transaction prices and time to sell. Other broker
properties such as proximity, experience and size have no significant effect on these
outcomes. Houses sold through a flat-fee broker obtain a 2.7 percent higher price
and sell significantly faster. The difference in transaction prices is almost unrespon-
sive to alternative specifications, such as conditioning on high- and low prices, the
type of house and the density of houses for sale in the same neighborhood. We
also do not find that homeowners who switch from a flat-fee broker to a traditional
broker obtain a significantly higher price than those who started with a traditional
broker. This rules out simple explanations such as differences in price or liquidity
of the house or selection of unobservable house characteristics. Finally, we look at
seller selection effects. We can conclude that sellers who used a flat-fee broker are
a lot better of than if they had used a traditional broker. For sellers who chose
a traditional broker, we cannot draw strong conclusions if they would have been
better of had they used a flat-fee broker as well (because there are too few sellers
with similar characteristics in the other group).
Our paper is related to Hendel et al. (2009), who look at the difference in price
and time on the market between the realtors’ MLS and a for-sale-by-owner (FSBO)
website in Madison. Given that all of our transactions take place on the same
platform, we are able to single out the impact of the additional services provided by
the traditional real-estate agents rather than a combination of services and quality
of the platform. Hendel et al. (2009) find that houses that are originally listed on an
FSBO website sell at a higher price no matter whether those houses are sold through
the realtors’ MLS or through the FSBO website, while we find that traditional real-
estate agents who receive a percentage of the selling price sell at a lower price than
the flat-fee agents. In section 5 we show that it is unlikely that our results are due
to censoring.
Our paper is also related to Bernheim and Meer (2013), who look at houses
sold at the university campus of Stanford. Similar to the paper of Hendel et al.
(2009), they compare FSBO sales with brokered sales. However, in contrast to
Hendel et al. (2009), but in line with our research, the Stanford study listed all
campus sales on one single open-access listing service, which is available regardless
of whether a broker is used or not. One caveat of the paper of Bernheim and
Meer (2013) is that the number of observations is low and the Stanford housing
market may not be representative for the total population because homeownership
at the campus is limited to Stanford faculty and some senior staff. Nevertheless, an
advantage of their analysis is that the sellers are a relatively homogeneous group,
which reduces the risk of seller selection into FSBO sales. Our group of sellers is
more heterogeneous, but also in our study the risk of seller selection is small because
sellers that use flat-fee brokers only perform a relatively simple task: providing the
viewings of the house. Therefore, differences in negotiation skills between buyers
and the potential stigma of FSBO sales cannot explain our results.
Finally, we contribute to the literature on potential agency problems for
delegated brokers in real estate (see Rutherford and Yavas, 2012, Han and Hong,
2011, Bergstresser et al. 2009, and Del Guercio et al., 2010). Our result of a 2.7
percent difference in transaction performance is substantial – and obviously larger
than the opportunity costs of the time spent when someone decides to sell the house
through a low-service flat-fee broker. The result for flat-fee brokers is similar in size
to that of brokers who sell a house that they own themselves; see Rutherford et al.
(2005) and Levitt and Syverson (2008b). The higher price obtained by a broker-
owner can be explained by the superior information available to the broker and the
higher effort level he or she expands. Barwick et al. (2017) find that houses that
pay low commission rates to the buyers’ agents are less likely to sell. They interpret
this as evidence for steering, i.e. buyers’ agents are less willing to intermediate for
a property with a low commission rate. Finally, our results are relevant for the
literature on the role of platforms in intermediation; see Hendel et al. (2009). Our
results suggest that once houses are listed on a platform, a simple structure of a flat
fee and minimal additional services performs very well in terms of a high transaction
price and fast sale.
We find no evidence that brokers with offices located in the proximity of the
seller perform better. This contrasts with earlier studies on the relation between
geographical proximity and investor performance by stock investors, hedge funds,
investors in municipal bonds and investors in mutual funds; see Teo (2009) and
Butler (2008). Ivkovic and Weisbenner (2005) find that households have a strong
preference for stocks that are geographically close. Moreover, they find that local
investors seem to have some degree of superior information.
The rest of this paper is organized as follows. Section 2 describes the insti-
tutional aspects of real-estate brokerage in the Netherlands. Section 3 describes the
data. Section 4 describes the empirical approach and estimates. Section 5 provides
additional robustness analyses. Section 6 discusses some potential explanations for
our main results. Section 7 concludes.
2 Real-estate agents in the Netherlands
In the Netherlands, real-estate agents can work independently, but most are mem-
bers of a real-estate association. The largest association is the National Association
of Real-Estate Brokers and Real-Estate Valuers (NVM), to which seventy percent
of all real-estate agents belong. In terms of transactions, NVM had a 75 percent
market share measured over 2010 and 2011.
In January 2001, NVM launched a website,, where all the houses of
its members are listed. Funda quickly became the dominant platform for potential
buyers, as it eliminated the need for a buy-side broker and made all the houses
and their details visible for free. In 2010, Funda started to list houses of non-NVM
agents as well.
Before 1999, NVM had a recommended fee of around 2 percent of the sales
price.4The recommended fee was abolished in 1999, under the threat of sanctions
from the anti-trust authority. Still, although the fees have been decreasing in terms
of the percentage of the sales price, anecdotal evidence suggests that many agents
continue to use fees close to 2 percent. In addition, brokers are reluctant to negotiate
the fee and, with the exception of the flat-fee brokers, they do not openly compete
on price.
Starting in 2005, flat-fee brokers entered the market as members of the bro-
kers association. They charge an up-front, fixed fee. At the moment of our research,
the fee of these brokers was in the range of 400 to 1300 Euro. In return, they list
4The recommended fee was 1.85 percent excluding VAT.
a house on Funda, advise on the list price and perform the negotiation. The major
difference with a traditional broker is that the viewing of the house is scheduled and
hosted by the seller.
In response to the Funda platform, some so-called for-sale-by-owner websites
have sprung up where sellers list their house directly, without the intermediation
of a broker. However, in contrast to other countries (such as the US), this has not
caught on in a significant way (Hendel et al., 2009, and Bernheim and Meer, 2013).
3 Data
The data set of transaction prices, obtained from the NVM, contains the properties
of houses and apartments sold between 1985 and 2011, as well as a unique identifier
for the real-estate agent. The data set does not contain objects that are rented out.
Note that the use of this data set implies that we do not observe transactions that
are not represented by a broker that is a member of the association. Nevertheless,
the benefit of using this data set is that all of our transactions since 2001 were listed
on the Funda website, which guarantees the use of one and the same platform for
every house sale in our data set.
Starting from 2,007,914 transactions, we remove observations with missing
house size (in square meters) and missing lot size (if not an apartment). Then, the
following filters are applied: we discard observations with a list price or transaction
price below ten thousand Euro and above one hundred million Euro, an absolute
percentage change in transaction price relative to list price of more than fifty percent,
a size of less than twenty square meters, and selling date equal to or before list date.
Also, we discard observations with other obvious errors. Furthermore, we remove
Table 1: Number of observations per house
Number of Number of
observations houses
per house
2 118,309
3 33,834
4 7,907
5 1,641
6 301
7 61
8 6
9 2
Total 162,061
observations with brokers for which we were unable to find their associated profile
(see below) and transactions on houses that have not sold since 2005 (i.e., the
year in which flat-fee commission brokers became active).5We also delete 327,097
transactions that represented a house that is only sold once in our data set and
therefore drops out of our analysis since we use house fixed effects in all of our
regression equations of Section 4. This leaves us with 380,252 observations which are
represented by 162,061 houses. Table 1 lists the number of observations per house.
The evolution of average prices and number of transactions appears in Figure 1.
3.1 Broker identification and characteristics
Each transaction in the data provides the identification number for the broker that
sold the house; this number has an associated broker profile on the online multi-
listing service. The profile gives the address, website and a short description of each
5We cannot remove all observations prior to 2005, as we need them to allow for house fixed
Figure 1: Transaction prices and volume, 2005-2011
This figure shows the price growth (solid line, left-hand axis) and volume (column, right-
hand axis) from January 2005 to June 2011 per quarter in which the sale occurs.
We define four other characteristics of brokers that may affect transaction
price and duration: (i) the fee strategy, (ii) proximity to the house, (iii) size and
(iv) experience.6
3.2 Fee strategy
The listing platform has a separate section that lists brokers. We use the search
phrase ”flat fee” to find the brokers who advertise with a flat-fee structure and
identify a total of thirteen brokers with a total market share of 2.3 percent of all
transactions since 2005 (i.e., the year in which they first became active). The brokers
are active throughout the country and this is reflected in the transactions (i.e., flat-
fee transactions are not limited to one or a few specific regions). All flat-fee brokers
that we find explicitly mention that they let the seller do the viewings.
3.3 Proximity
To find the geographical location of the broker, we take the address on the broker’s
profile page and feed it into a geocoding service in order to obtain the xand y
spatial coordinates. The geocoding was done in 2011 and 2012 to minimize the
measurement errors from relocations of real-estate agents. We compute the absolute
distance between each object and its broker using the xand yspatial coordinates.
The dummy variable ‘Close-by’ is 1 if the house is within the smallest 20th percentile
of distances between houses and brokers, which is approximately 800 meters.
6We use dummy variables to reduce noise, but the results remain qualitatively similar if we use
log-linear variables for proximity, size and experience.
Table 2: Summary statistics of the dependent variables.
Mean Median Standard
Initial list price 208,256 189,500 107,738
Last list price 205,021 189,000 105,291
Transaction price 197,244 180,000 100,700
Percentage difference
with last list price -3.7 -3.4 3.7
Days to sell 115 64 138
3.4 Size
We compute the number of transactions per broker, per year, and determine large
brokers as those with a size above the 80th percentile.
3.5 Experience
We define the experience of a broker as the difference between the current year and
the first year that a broker has sold a house. The dummy variable ‘Experienced’ is
1 if the broker associated with the transaction has an experience which is above the
80th percentile.
Table 2 presents the summary statistics of the transactions in our data set. We
observe that the average difference between the final list price and the transaction
price is -3.7 percent. Note that, in contrast to the situation in the US, the list price
does not involve any legal obligation to sell in the Netherlands. The average time
on the market is 115 days, while the median duration is 64 days. The high mean
relative to the median indicates that the distribution of sales times is skewed to the
right. This is consistent with stock-flow models of the housing market; a new seller
either sells fast to buyers from the stock or, if she does not immediately sell, she
Table 3: Summary statistics of the regressors
Lot size (only houses) 164
Surface area (m2) 107
Volume (m3) 313
Distance to broker (in miles) 2.78
Flat-fee broker 0.017
Apartment 0.370
Elevator 10.5
Number of floors 2.24
Number of rooms 4.16
Attic 0.242
Loft 0.073
Roof terrace 0.06
Has more than 1 toilet 0.780
Has more than 1 bathroom 0.023
Has a garage 0.228
Garden maintenance (subjective 1-5) 3.35
Maintenance inside (subjective 1-10) 7.02
Maintenance outside (subjective 1-10) 7.04
Insulation level (subjective 1-5) 1.96
Modern heating 0.907
Has open view 0.273
Next to busy road 0.036
Basement 0.023
Is a monument 0.005
Quarter of sale
January-March 26.8
April-June 27.0
July-September 24.1
October-December 22.1
awaits the arrival of new buyers (flow) (see Coles and Smith ,1998).
Table 3 lists the descriptive statistics of our regressors. The average distance
to the broker is 2.78 miles with a median of 1.19 miles. This indicates that real-estate
agents operate in a local market. A total of 12,136 transactions are carried out by
brokers that use a flat-fee strategy, which is 1.7 percent of the total transactions
that we use for our panel estimation for the entire period. The market share of
flat-fee brokers is 2.3 percent in the sample of houses sold since 2005. Our sample
contains both multi-floor houses and apartments. A seasonal effect is visible in the
lower number of houses sold in the fourth quarter (22 percent) relative to the first
(27 percent).
4 Empirical Analysis of the Broker Effect
4.1 Panel Estimation
We use the following model to investigate the effect of broker characteristics on the
transaction price and sales time:
log Yit =Xitβ+kp(i)t+Zit γ+ηi+µit +Uit,(1)
where Yit is the dependent variable of interest (either the transaction price or the
sales time) of house iin year tin province p;Xit contains the characteristics of
house iat time t, including the quarter in which it is sold (see Table 2). kpt captures
region-specific variation in annual prices and Zit contains the four broker dummies:
flat fees, located close by, large and experienced. The final three terms are the
fixed house effect (ηi), the time-varying house-specific effect (µit), and the residual
(Uit). The fixed house effect contains anything that is affecting the sales price, but
which is not captured in Xit and which does not change over time. The time-varying
house-specific effect µit contains any time-varying house characteristics not captured
by Xit and ηi, such as the time between two sales, buyer and seller characteristics,
and unobserved characteristics of the house not captured by the other variables.
The error term Uit is assumed to be independent of all observed and unobserved
We control for seasonality by including dummy variables for the different
quarters as controls. We use an apartment-specific surface measure (m2) as apart-
ments lack some characteristics that houses have, such as the lot size and garden,
which might influence the shadow price of surface for apartments. As the time
between consecutive sales might affect pricing, we also include a variable that mea-
sures the time in years since the previous sale of the house. All quantity variables in
Xit are in logs. We use twenty-two regional dummy variables for the construction of
kp(i)tin (1): ten for the largest cities and twelve for the provinces in the Netherlands.
The error term, Uit, is assumed to be independent and identically distributed.
The model in (1) is similar to that of Levitt and Syverson (2008b) and Rutherford
et al. (2005), except that we have the broker variables as additional explanatory
The parameters in equation (1) can be estimated using fixed effects under
the condition that time-varying house-specific effects are absent or that they are
not correlated with any of the observed characteristics, including the seller’s choice
to use a flat-fee real-estate broker. This is also the strategy used, for example, in
Hendel et al. (2009). We discuss these assumptions in the next Sections.
Our results are presented in Tables 4 to 6, where we have multiplied the
Table 4: Estimation results for the log transaction price
Estimated coefficients on the broker dummy variables for regressions
of the transaction price. The sample period is 1985-2011, using
houses that sold at least once since 2005. Only the coefficients
on the broker dummies are reported (multiplied by 100). Neigh-
borhood clustered standard errors appear between parentheses. *,
**, *** denote significance at the 90%, 95% and 99% levels, respectively.
(1) (2) (3) (4)
Flat-fee broker 2.38∗∗∗ 2.77∗∗∗ 2.73∗∗∗ 3.03∗∗∗
(0.39) (0.49) (0.49) (0.54)
Close by 0.032 0.033 0.12
(0.29) (0.29) (0.33)
Experienced 0.740.720.53
(0.44) (0.43) (0.45)
Large 0.12 0.10 0.12
(0.20) (0.20) (0.22)
Time between (in log) 0.25∗∗∗ 0.30∗∗∗
(0.051) (0.053)
House fixed effects Yes Yes Yes Yes
House characteristics Yes Yes Yes No
Region ×year dummies Yes Yes Yes Yes
R2within 0.935 0.935 0.935 0.920
coefficients by 100. Table 4 lists the results for the transaction prices and shows
that controlling for house fixed effects, region-year interactions and agent charac-
teristics, a flat-fee agent is associated with a 2.7 percent higher transaction price.
Our estimates imply that the benefits for the mean seller in the period 2005-2011
(house value of 197,244 Euro) of using a flat-fee broker are substantial. Apart from
the lower fee paid by sellers using a flat-fee broker, sellers gained more than 5,300
Euro in terms of a higher transaction price relative to those who used a traditional
Table 5 lists the results for the list prices. In line with the results presented
in Bernheim and Meer (2013), we find that the lower selling prices can be completely
Table 5: Estimation results for the log initial list price
Estimated coefficients on the broker dummy variables for regressions
of the transaction price. The sample period is 1985-2011, using
houses that sold at least once since 2005. Only the coefficients
on the broker dummies are reported (multiplied by 100). Neigh-
borhood clustered standard errors appear between parentheses. *,
**, *** denote significance at the 90%, 95% and 99% levels, respectively.
Flat-fee broker 2.62∗∗∗
Close by 0.13
Experienced 0.85
Large 0.36
Time between (ln) 0.25∗∗∗
House fixed effects Yes
House characteristics Yes
Region x year dummies Yes
R2within 0.932
explained by the lower list prices.
Table 6 lists the results for the time to sale. The higher transaction price
that flat-fee brokers realize is, surprisingly, associated with shorter sales times. The
coefficient is large, at -18%, suggesting a sales time that is roughly 21 days shorter
than the mean sales time of 115 days for all transactions since 2005. The results in
Table 6 suggest that some of the other broker characteristics are also important for
sales time. Large and nearby brokers sell houses faster. Our data suggests that there
is no obvious trade-off between sales time and transaction price. This contrasts with
Levitt and Syverson (2008b) and Rutherford et al. (2005), which have longer sales
times associated with a higher price. Finally, note that for the time on the market,
Table 6: Estimation results for the log time to sale
Estimated coefficients on the broker dummy variables for regressions
of the transaction price. The sample period is 1985-2011, using
houses that sold at least once since 2005. Only the coefficients
on the broker dummies are reported (multiplied by 100). Neigh-
borhood clustered standard errors appear between parentheses. *,
**, *** denote significance at the 90%, 95% and 99% levels, respectively.
(1) (2) (3) (4)
Flat-fee broker 24.0∗∗∗ 18.3∗∗∗ 18.4∗∗∗ 18.1∗∗∗
(4.32) (4.60) (4.60) (4.62)
Close by 5.09∗∗∗ 5.09∗∗∗ 5.03∗∗∗
(1.92) (1.92) (1.92)
Experienced 3.85∗∗ 3.83∗∗ 3.78∗∗
(1.54) (1.53) (1.53)
Large 7.15∗∗∗ 7.12∗∗∗ 7.01∗∗∗
(1.78) (1.78) (1.78)
Time between (in log) 0.50∗∗∗ 0.51∗∗∗
(0.15) (0.15)
House fixed effects Yes Yes Yes Yes
House characteristics Yes Yes Yes No
Region ×year dummies Yes Yes Yes Yes
R2within 0.146 0.146 0.147 0.145
the fit and precision is less than for the price estimates.
Leaving out the house characteristics, as in column (4) of Tables 4 and 6
gives almost identical estimates and the reduction in R2is minimal. It shows that
the year dummy variables pick up almost all of the price variation that is not related
to the broker. This is explained by the fact that the most important characteristics,
such as surface and volume of the house, do not change much over time.
We also test for higher order and interaction effects of our controls, and our
main result remains qualitatively similar (i.e. the coefficient for a flat-fee strategy
remains significant and in the range of 2 to 3 percent). The same holds for removing
the largest flat-fee broker from the sample (i.e., our results are not caused by having
one particularly high-performing broker).
The positive coefficient for the (log) time between consecutive transactions in
Tables 4 and 5 indicates a seller-specific effect. This could be caused by older sellers
who are less likely to be credit constrained and thus more patient; see Albrecht et
al. (2016). This is consistent with a positive relation between the sales time and
the time between the two sales; see Table 6.
Finally, note that the house fixed effects control for the exact location of
the house and other subjective elements that are difficult to measure quantitatively.
Moreover, the houses are all listed on the same online platform, so that the network
effects of different platforms or sellers are not driving our results. Thus, our results
reported in Tables 4 to 6 cannot be explained by an information effect: the sellers
involved in these transactions have exactly the same information.
5 Robustness
5.1 Interaction effects
The hedonic model in (1) might be misspecified with regard to nonlinear relations
between house characteristics and (log) prices. We can control for this by introducing
interactions of variables of interest with the flat-fee dummy.
The first interaction concerns price. Expensive houses might be underpriced
in the log-linear hedonic model and sold more often by a flat fee. In that case, the
coefficient in the full sample might be driven by a limited number of expensive houses
with a successful house sale using a broker with a flat-fee strategy. Also, given that
a traditional fee is a percentage of the transaction price, the monetary incentives to
choose a broker that uses a flat-fee strategy are higher for more expensive houses. In
order to investigate this, we interact an above-median price dummy with the flat-fee
broker dummy.
A second concern is that apartments and houses in high-density areas could
be overrepresented in the sample of houses that are sold through the use of a flat-fee
broker. Apartments are easier to price, since they have fewer unique characteristics,
making it possible to find almost identical objects (such as apartments in the same
building that have been sold before). Likewise, sellers in neighborhoods with a high-
density of houses might find it easier to set a list price based on comparable objects
and be more inclined to choose a flat-fee broker. We control for these effects by
interacting the flat-fee dummy with the dummy variable for apartments and the
dummy variable for above-median neighborhood densities.
A third concern is that sophisticated sellers are more likely to sell using
a cheaper flat-fee real-estate agent. We expect that this selection effect would be
largest in the earliest years of the introduction of flat-fee real-estate agents, since the
most sophisticated sellers are also the ones who are most likely to be early adopters
of the new selling strategy. Moreover, the number of sales made with flat-fee real-
estate agents has been increasing over time, which immediately implies a decrease
in the selectivity of the sophisticated sellers. This hypothesis implies that we should
expect a declining impact of flat-fee brokers since their introduction in 2005. We
test this by interacting the flat-fee dummy with a dummy that is 1 in the years prior
to 2008.
A fourth issue is that traditional real-estate agents could be exerting more
effort in the fourth quarter in order to meet their annual sales target. This effect is
absent under a flat fee, where fees are earned up-front. We therefore interact our
flat-fee dummy with a dummy for the fourth quarter.
A final concern is that the distribution of sales times could be more fat-tailed
for those sales involving the help of a flat-fee broker. That is, traditional brokers
would be more effective for difficult-to-sell houses, while a flat-fee structure would
be better for liquid properties. To test this, we interact with a dummy variable
that is 1 if the sales time is higher than the median. Note that the interpretation
of this variable is complicated by the fact that sales time is endogenous. That is,
unobserved characteristics that affect the sales time also affect the selling price of
the house. Nevertheless, due to the expected negative relationship between the two
outcome variables, the absolute value of the coefficient estimates can be interpreted
as an upper bound of the real effect.
Table 7 reports the estimation results for transaction prices with interaction
effects. The conclusion that can be drawn from this Table is that in all alternative
specifications, the positive flat-fee effect remains. The only statistically significant
terms are the ones with sales time and the fourth quarter dummy. The sales-time
effect is such that flat-fee houses sell 4.1 percent faster. For longer sales times, the
performance is reduced to 1.3 percent, but as stated in the previous paragraph, this
can be interpreted as a lower bound. If anything, this indicates that even houses
with a long time to sale obtain a higher price when using a flat-fee broker. The
fourth-quarter effect suggests that traditional brokers put more effort into selling a
house in the fourth quarter, reducing the performance gap with fixed-fee brokers to
1.5 percent (3.07 - 1.64). This supports an end-of-year effect on the part of real-
estate agents aiming to reach a sales target. Such effects exist in many industries
where compensation schemes depend on yearly sales in a non-linear way; see Oyer
(1998). To summarize, the evidence points to a sizable and significantly positive
Table 7: Interaction effects
Estimated coefficients on the broker dummy variables for panel regressions
of transaction price on house- and broker characteristics. The sample
period is 1985-2011, using houses that sold at least once since 2005. Only
the coefficients on the broker dummies are reported, multiplied by 100.
Clustered standard errors appear between parentheses. *, **, *** denote
significance at the 90%, 95% and 99% levels, respectively.
(1) (2) (3) (4) (5) (6) (7)
Flat-fee broker 2.55∗∗∗ 2.87∗∗∗ 2.71∗∗∗ 4.11∗∗∗ 3.07∗∗∗ 2.86∗∗∗ 4.68∗∗∗
(0.72) (0.61) (0.78) (0.58) (0.51) (0.62) (1.28)
Close by 0.033 0.033 0.033 0.033 0.033 0.033 0.032
(0.29) (0.29) (0.29) (0.29) (0.29) (0.29) (0.29)
Experienced 0.720.720.720.730.720.720.73
(0.43) (0.43) (0.43) (0.43) (0.43) (0.43) (0.43)
Large 0.10 0.10 0.10 0.10 0.10 0.10 0.10
(0.20) (0.20) (0.20) (0.20) (0.20) (0.20) (0.20)
Time between (ln) 0.25∗∗∗ 0.25∗∗∗ 0.25∗∗∗ 0.25∗∗∗ 0.25∗∗∗ 0.25∗∗∗ 0.25∗∗∗
(0.051) (0.051) (0.051) (0.051) (0.051) (0.051) (0.051)
x High price 0.28 0.044
(0.81) (0.97)
x Apartment 0.31 0.30
(0.82) (0.97)
x High density 0.027 0.070
(0.85) (0.90)
x Long sales time 2.77∗∗∗ 2.75∗∗∗
(0.62) (0.61)
x Fourth quarter 1.64∗∗ 1.52∗∗
(0.74) (0.75)
x Sold before 2008 0.24 0.30
(0.68) (0.74)
R2within 0.936 0.936 0.936 0.936 0.936 0.936 0.936
effect associated with a flat fee on the part of the seller that is not explained by
interactions with other variables.
5.2 Sellers switching agents
As in the analysis of Hendel et al. (2009), another way to investigate the selection
effect of sellers is to look at the sellers who initially started with one type of broker
(for example, a flat-fee broker) and then switched to another type (say, a traditional
broker), for the same property. If the switching between brokers is random, then
we are able to eliminate the selection effects by comparing those house sales that
involved a switch between broker types and the ones that did not. In order to
investigate this, we employ an additional data set that is obtained from screen-
scraping the listing site where all houses are advertised. The data were collected in
the period 2004-2010; an earlier version of the data set was used in Gautier et al.
We use the identification number of the first real-estate agent listing the
house online, and indicate a sale as a “switch” when this agent is different from the
agent at the time of sale. We find 3,680 transactions where the agent changed from
a flat-fee type to a traditional broker, and 80 transactions where the agent changed
from the traditional type to one with a flat fee.
The low number of switchers from traditional agents may be related to the
difference in switching costs: traditional agents usually charge a termination fee as
a compensation for the loss of income caused by the cancellation of the contract.
The opposite occurs for sellers with a flat-fee agent: since they have already paid
the fee up-front, their costs are sunk and hence there are no further monetary costs
involved in changing to another agent. The low number of switchers from traditional
to flat-fee is in line with Hendel et al. (2009), who also find a very low number of
switches from sellers represented by a real-estate broker using the MLS and sellers
Table 8: Sellers switching real-estate agents
Estimated coefficients on house- and broker characteristics. The sample period is
1985-2011, using houses that sold at least once since 2005. Only the coefficients
on the broker dummies are reported, multiplied by 100. Clustered standard
errors appear between parentheses (by neighborhood). *, **, *** denote
significance at the 90%, 95% and 99% levels, respectively.
Dependent variable : Transaction price Time to sale
(1) (2) (3) (4)
Flat-fee broker 2.73∗∗∗ 2.76∗∗∗ 18.4∗∗∗ 18.7∗∗∗
(0.49) (0.49) (4.60) (4.61)
Close by 0.033 0.034 5.09∗∗∗ 5.11∗∗∗
(0.29) (0.29) (1.92) (1.92)
Experienced 0.720.723.83∗∗ 3.83∗∗
(0.43) (0.43) (1.53) (1.53)
Large 0.10 0.10 7.12∗∗∗ 7.11∗∗∗
(0.20) (0.20) (1.78) (1.78)
Time between (ln) 0.25∗∗∗ 0.25∗∗∗ 0.50∗∗∗ 0.50∗∗∗
(0.051) (0.051) (0.15) (0.15)
Seller left flat-fee broker (N=3680) 2.94 22.7
(3.90) (49.6)
Seller came to flat-fee broker (N=80) 0.48 13.2∗∗
(0.48) (5.40)
R2within 0.936 0.936 0.147 0.147
who sell their own houses.
We create two additional dummy variables for the transactions: one for a
transaction that started with a traditional agent and ended with a flat-fee agent, and
one for a transaction that started with a flat-fee agent and ended with a traditional
The effect on prices is not affected much and for time to sale, there is only
a significant effect for sellers who use a flat-fee agent. This is in sharp contrast
with the results of Hendel et al. (2009), who find a strong positive effect for houses
that are initially listed on the for-sale-by-owner website. Moreover, they conclude
that whether the property gets ultimately sold by the sellers themselves makes no
difference with respect to the price, if one controls for the fact that the property was
originally placed on the for-sale-by-owner website. We find the opposite: houses that
were originally represented by a flat-fee broker and ultimately sold by a traditional
broker, sell at an insignificantly lower price and are longer on the market.
5.3 Unsold items
One concern is that our results are conditional on the outcome that the houses are
sold. Unfortunately, our main data set does not contain houses that were placed on
the Funda website but were never sold. However, as already stated in Section 5.2, we
also have a data set that we obtained from screen-scraping the Funda website. We
can also use this website to investigate how many houses were posted on the website
but were never sold. One risk of this data set is that there may be some houses
that we are not able to match with houses in our original data set and therefore we
mistakenly consider them as unsold. Therefore, we use this method as a robustness
check only. Another caveat of the data set is that we only have data for the period
2004 to 2008.7
We find that 69.1 percent of the houses that ever appeared on the website
got ultimately sold. In order to investigate whether relatively many sellers using a
flat-fee broker withdrew their houses from the market, we estimated a logit model
with a successful sale as dependent variable. Because our period of analysis is short
and because we did not want to run the risk of not being able to match our (unsold)
7We have the spidered data for a longer period but we do not want to run the risk that some
houses were not sold yet at the latest date of our original data set.
houses with houses that were sold in the past, we decided not to include any house
fixed effects. The results of our logit regression are found in Table 9. We find that
houses that were placed on the website by a flat-fee broker have a lower likelihood to
be sold. The average derivative results in a 5.68 percent lower probability of a house
being sold by such a broker. Apparently, even though sellers that use the services
of a flat-fee broker sell more quickly (conditional on a successful sale), they also
seem to quit their sales efforts faster than the sellers of houses who are traditional
An important question is whether this biases our final results. Of course it is
impossible to know the counterfactual for unsold houses. However, it is likely that for
those houses, prices would have been on average lower. In order to investigate this,
we use the selection correction method in non-separable regression models developed
in Fernandez-Val, et al. (2017). This correction method is based on the following
log Yi=g(Xi, Zi, Ui),
log Ti=h(Xi, Zi, Wi),
where gand hare unspecified functions. We assume here that Xirepresents the
usual house characteristics and that Zionly indicates whether a sale was made by a
flat-fee broker. The stochastic terms Uiand Wirepresent unobserved heterogeneity
for respectively the selling price and the time on the market. These error terms are
allowed to be dependent upon each other. The outcome variable Yiis price and Tiis
time on the market. The only assumptions that we make here are that hincreases
with Wiand that Xiand Ziare independent from both Uiand Wiin case that all
houses prices are observed. However, we assume that Yiis only observed in the case
that Ti< Cifor some random variable Ci, which is independent of Uiand Wi. For
this case, it can be shown that the distribution of ViFT(Ti|Xi, Zi) is a control
variable for Ui|Xi, Zi, Ti< Ci. We estimate the control variable using a mixed
proportional hazards rate model with a piecewise constant baseline hazard and use
the integrated hazard to calculate the distribution function (see Donald et al., 2000).
Then, we estimate the local average structural function, µ(x, z, v) = E(log Yi|Xi=
x, Zi=z, Vi=v) by the predictions of a non-parametric series regression of log Yi
on Xi,Ziand the control variable Vi. The average treatment effect on the treated,
EXi,Vi|Zi=1(µ(Xi,1, Vi)µ(Xi,0, Vi)),
can then be estimated by averaging over the values of Vi,
i=1 bµ(Xi,1,b
Vi)1(Zi= 1),n
1(Zi= 1),
where nis the number of observations. This procedure is valid as long as we assume
that houses are more likely to be withdrawn from the market (for any elapsed time
on the market) when a flat-fee broker is used than when a traditional broker is used.
This seems a valid assumption based on our earlier results. In addition, we need a
rank invariance assumption. That is, houses that sell fast with a flat-fee broker (due
to favorable unobserved characteristics) should also sell fast for traditional brokers
and vice versa. Note that we do not need an exclusion restriction for this case.
The idea behind our method is simply that we impose the apparently more
restrictive truncation process of the flat-fee brokers on the duration distribution of
Table 9: Results of the unsold items
Estimated coefficients on the broker dummy variables. The sample
period is 2004-2008. Only the coefficients on the broker dummies
are reported (multiplied by 100). Standard errors appear between
parentheses. *, **, *** denote significance at the 90%, 95% and 99%
levels, respectively.
Sold uncor- cor-
rected rected
Flat-fee -0.275 -0.016 3.02 2.88
(0.019) (0.015) (0.25) (0.24)
the traditional brokers. In addition, we also impose this through the relationship
between the unobserved components (i.e. houses that take longer to sell have in
general less attractive unobserved characteristics) on the price distribution of these
traditional brokers. This gives us the counterfactual distribution that we would ob-
serve in the case that traditional brokers would have terminated their sales effort at
the same speed as the flat-fee brokers do. This is identified based on the assumption
of the higher level of truncation for the flat-fee brokers.
The results of this exercise are reported in Table 9. The second column of
that table reports the coefficient of the mixed-proportional hazards rate model. The
coefficient is negative but insignificant and extremely small, implying that when
taking the unsold items into account, both broker types sell at almost the same
speed. The average treatment effect equals 3.02 percent in the case that we do not
correct for selective attrition. This amount is only slightly reduced to 2.88 percent
in the case that we use the correction method. We can conclude from this that, even
though the traditional real-estate agents are less likely to withdraw difficult-to-sell
houses from the market, this explains only a small part of the difference in selling
prices between these two types of agents.
5.4 Seller-selection and marginal effects
The broker effect could be caused by unobserved seller sophistication that is corre-
lated with both broker choice and sales performance. We analyze these potential
seller selection effects by using an instrumental variable approach. Let V0,it and V1,it
be residual terms for the impact of the price of respectively a traditional real-estate
broker and a flat-fee broker. Let Iit be an instrument that has an impact on the
choice for a flat-fee broker but not on either V0,it or V1,it. Let Ψ be an unspecified
function of this instrument. Let Wit be a random variable representing the unob-
served terms that have an impact on the choice for a flat-fee broker. We allow for
the fact that Wit depends on both V0,it and V1,it . Without loss of generality, we
assume this variable to follow a standard uniform distribution. We use the following
specification for the dummy variable that indicates whether a sale was by a flat-fee
Zit =
1(Xitθ+ Ψ(Iit )Wit 0) if tt0,
0 otherwise,
where in this subsection Zit is the indicator variable for whether a flat-fee broker is
used. Hence, the larger Wit, the less likely the seller is to use a flat-fee broker. Since
we expect that individuals who use a flat-fee broker are potentially also the ones
that are most likely to benefit from it, we conjecture that the difference between V1,it
and V0,it and hence the individual effect of using a flat-fee broker, decreases with
Wit. Let Uibe a fixed-house effect and define γto be the average impact of choosing
a flat-fee broker if everybody decides to choose a flat-fee broker. In line with the
evaluation literature, we call this the average treatment effect. The individual effect
equals γ+V1,it V0,it and is hence allowed to vary between individuals, i.e. some
sellers benefit more from a flat-fee broker than others. Now we can generalize (1)
Yit =
it =Xitβ+γ+Ui+V0,it if Zit = 1,
it =Xitβ+Ui+V1,it otherwise.
Our model assumptions imply that we can estimate Zit using a linear probability
model for the period after 2005. The predictions of this linear probability model can
be substituted into the second stage regression (where we can predict the probability
to be equal to zero for the years before 2005). This is a standard instrumental
variable approach.
We use the number of traditional real-estate agents in the neighborhood
of the house as an exclusion restriction. Note that traditional real-estate agents
operate locally, while flat-fee agents typically serve the real-estate market all over
the Netherlands. Hence, if a high number of traditional real-estate agents are located
very close to the house of the seller, then their visibility is higher, which increases
the likelihood that the seller uses such a traditional real-estate agent. Of course,
the location choice of real-estate agents is partly endogenous so we have to correct
well for neighborhood effects, since a high number of real-estate agents can also
reflect the fact that the market is doing very well in that neighborhood.8Note also
that this only concerns changes in the neighborhoods, since neighborhood effects
themselves are already fully contained in the house-fixed effects. We also perform
a robustness check by looking at the relative number of real-estate agents in a very
small neighborhood around the house, in comparison to a more roughly defined
region. Finally, the concentration of real estate agents in a neighborhood reflects
8We assume here that whether individual sellers are good in selling their own house or not is
private information and so cannot affect the location choice of real-estate agents.
Number Frequency
0 460,935
1 53,768
2 19,668
3 8,090
4 3,309
5 1,305
>6 1240
Table 10: Distribution of the instrument
the level of competition among the traditional brokers in that neighborhood and
this affects the choice for a flat-fee broker as well.
We use a distance of 250 meters in our baseline model. The distribution of
the instrument is listed in Table 10. Around 84 percent of the transactions do not
have any close-by traditional real-estate agent. This is not surprising given the short
distance of 250 meters. Still, there is quite a number of transactions with more than
one close-by traditional real-estate agent. The results of the first-stage regression
are reported in Table 11. We find our instrument to be highly significant as can
be seen from Table 11. Moreover, we obtain the expected sign. That is, a higher
number of traditional real-estate agents in the neighborhood has an unambiguously
negative impact on the likelihood that a flat-fee agent is used.
The second-stage regression results are reported in the first column of Table
12. The coefficient of the impact is about three times as large as those reported in
Table 4. We use the weighted bootstrap with a sample size of 1000 in order to ob-
tain confidence intervals. Our sample weights are based on the standard exponential
distribution. See for example Chernozhukov, Fernandez-Val and Melly (2013), for a
discussion of the weighted bootstrap. Based on our results from the weighted boot-
strap, the coefficient for the impact on the price is no longer significantly different
Use of
flat-fee broker
# of nearby traditional brokers (×100) -0.9673
(# of nearby traditional brokers)2(×100) 0.2181
(# of nearby traditional brokers)3(×100) -0.0163
Positive # of nearby traditional brokers 0.0041
Table 11: Linear probability model estimation of the likelihood to chose a flat-fee
Log price Log time to sale
Propensity scores
Flat-fee broker 8.26 -3.39 -174.6 -118.9
(-1.52, 21.48) (-14.32, 7.54) (-266.6, -122.7) (-118.9, -42.6)
(Flat-fee broker)2888.5 -1391
(877.5, 899.4) (-1467, -1315)
(Flat-fee broker)3-9572 18430
(-9583, -9561) (-18510, -18360)
Table 12: Second-stage regression estimates.
from zero at the 95 percent level. Our estimated p-value equals 0.14. Note that the
lower significance level is entirely based on a higher standard error and not on the
value of the point estimator. Also note that even though it is possible to calculate
the analytical asymptotic standard errors for this case, the use of bootstrap methods
is well known to have a higher asymptotic accuracy and to be more robust against
outliers (see also Young, 2017).9
There is a large literature on the interpretation of the instrumental variable
estimator (see Heckman and Vytlacil, 2005 among others). In a linear and separable
9We use the asymptotic refinement as proposed by Chernozhukov, Fernandez-Val and Melly
(2013). Indeed, the estimated IV standard error suggests a p-value which is less than 1 percent.
model, as we have here, it can be interpreted as the estimator of γand hence as the
(counterfactual) average in case everybody decides to use a flat-fee broker. However,
this assumes that our sample contains individuals that almost surely use a traditional
broker and individuals that almost surely use a flat-fee broker. The first assumption
is not very restrictive, given the high number of sellers that use a traditional broker.
On the contrary, the second assumption is harder to defend. In fact, the estimated
propensity scores in the first-stage regression of our analysis are no larger than 0.24,
making it extremely unlikely that this restriction is satisfied. Hence, it is hard to
defend that our instrumental variable estimator is able to consistently estimate the
average effect. Therefore, it is better to focus on objects which are identified even
in the case that the full support restriction of the propensity scores is not met. An
example of such an object is the marginal treatment effect of Heckman and Vytlacil
(2005). This estimator attempts to estimate the following object
M T E (x, w)EY1
it Y0
it |Xit =x, Wit =w(4)
The marginal treatment effect measures the average impact of an individual trans-
action with a level of Wit exactly equal to w. Since Wit is negatively related to seller
quality, we conjecture that the marginal treatment effect decreases with w.
The results of the local instrumental variable estimator, i.e. the estimation
of E(Yit|Xit =x, P (Xit, Iit ) = p), are listed in the second column of Table 12. Figure
2 plots the estimated marginal treatment effects based on these estimates (which are
the first-order derivative of the polynomial). We find that the marginal treatment
effect is largest for small values of the propensity score. This is as expected since
a small value measures the impact of a flat-fee broker for a transaction with a very
small value of Wit and as discussed above, this implies most likely a seller who has
a high benefit from using a flat-fee broker. The value starts to drop quickly after
a propensity score of 0.03 and becomes even negative for scores equal to 0.06 or
higher. Note that – by the definition of Wit – only 6 percent of the observations have
a propensity score below 0.06. Nevertheless, this does not mean that the majority
of the sellers using a flat-fee broker are worse off when using such a broker than if
they used a traditional type of broker. On the contrary, due to the construction
of our model, every individual using a flat-fee broker must have had a level of Wit
lower than their corresponding propensity score. We find that 79 percent of the
individual transactions that used a flat-fee broker had a level below 0.06. Hence,
at least 79 percent of these individuals must have benefited from using the flat-fee
broker. Moreover, 99.6 percent of the observations have a value below 0.1 and even
though the predicted loss of these individual transactions is enormous, there must
be very few of them.
It is much harder to say anything about the transactions that did not use a
flat-fee broker. Due to the construction of our model, their realizations of Wit must
be larger than their propensity scores, implying that these realizations are generally
larger than those reported in Figure 2. Hence, these individuals would have lost a
large amount in the case that they would have used a flat-fee broker. However, we
should realize that the predictions above 0.1 are based on very few observations:
only 0.04 percent of the observations have such a high propensity score.
The average treatment among the treated, T T , is defined as the average
impact among those that have chosen to use a flat-fee broker. It can be shown to
0.02 0.04 0.06 0.08 0.1
0.02 0.04 0.06 0.08 0.1
Figure 2: Marginal treatment effect of the log price for different values of Wit in the
identified area (gray areas are the 95-confidence bounds based on weighted boot-
equal (Heckman and Vytlacil, 1999)
T T =ZX |Zit=1 ZW(x)|Zit =1
M T E (x, w)P(Pit w|Zit = 1, Xit =x)
E(Pit|Zit = 1, Xit =x)dwdFX(x|Zit = 1).
We show in the Appendix that it is possible to derive an estimator based on this
equation. We obtain a value equal to 0.1406 for the average treatment effect among
the treated for the log sales price. Based on a bootstrap sample of 1000, we obtain
a lower bound of the 95 percent confidence interval equal to 0.0216 and the upper
bound equals 0.2595.10 This implies that those who decided to use a flat-fee broker
obtained a reasonably large benefit from using such a broker.
Table 12 lists the results of model (4), but now with the log of time to sale
instead of the log price. We find an extremely large value for flat-fee broker and even
though the reported confidence interval is also large, it is also significantly different
10A formal proof of the statement that bootstrap sampling results in consistent estimates of the
confidence intervals requires empirical process theory. We consider this as outside the scope of this
from zero at any reasonable level of significance.
As a final exercise, we look at an alternative instrument that measures the
relative number of traditional real-estate agents that are located very close to the
object being sold, in comparison to the number of traditional real-estate agents
that are available in a more distant area. The reason for this robustness check is
that there is a risk that areas that have a lot of traditional real-estate agents are
simply better areas being equipped with more banks, railway stations, shops as well
as real-estate agents. Note that this risk only affects changes in areas and that
we partly take these problems into account by using region-specific time dummy
variables. Also note that the described mechanism generally results in a downward
bias of our estimates as long as we expect that the endogenous location decisions
of traditional real-estate brokers are affected in favor of areas that have been doing
relatively well in the period of analysis. Nevertheless, the robustness check is able to
identify any problems of the grouping of our regions. In order to do this, we still use
250 meters as the distance to traditional real-estate agents but divide this number
by the number of traditional real-estate agents that have an office at a distance
less than 5 kilometers from the house. We do not present our first-stage regression
estimates for this robustness check. The results of the second-stage regression are
reported in Table 13. We find much larger impacts of the use of a flat-fee broker.
Another potential selection effect is that sellers with high opportunity costs
for conducting the viewings choose a traditional broker to do it for them. However,
based on the price difference of 2.7 percent and a transaction fee for the traditional
real-estate agents of 2 percent, our results indicate a difference of roughly 5 percent of
the sales price. For an average sales price of 200,000 Euro, this implies a difference of
10,000 Euro in terms of the two different types of brokers. It is unlikely that almost
Log price Log time to sale
Propensity scores
Flat-fee broker 40.14 45.69 -890 -729
(28.32, 58.89) (21.04, 73.28) (-1153, -628) (-1029, -420)
(Flat-fee broker)21032 -3211
(952.9, 1005) (-3516, -2907)
(Flat-fee broker)3-18070 -2959
(-16570, -16520) (-4478, 3869)
Table 13: Robustness check of the second-stage regression estimates for the log price
and log time to sale.
98 percent of the sellers (i.e. those using a traditional real-estate broker), have such
a high level of opportunity costs. To place the 10,000 Euro into perspective, note
that the marginal tax rate in the Netherlands equals 42 percent for a median income
of 35,000 in 2015. Hence, this explanation requires that the opportunity costs must
be roughly half a year of labor income, which seems unlikely.
Better negotiation skills by some sellers, or tougher bargaining are not likely
explanations because both types of brokers advise on the list price and perform the
negotiations at arms-length. Also, we find that using a flat fee has a similar effect
on list prices as on transaction prices; it is therefore not the case that list prices are
used more strategically by one type of broker. Finally, our findings hold for different
subsets of sellers and neighborhoods.
From this section we can conclude that sellers who use flat-fee brokers were
much better off than if they would have used traditional brokers. Since there are
much more transactions with traditional brokers we cannot draw strong conclusions
on whether sellers who used a traditional broker were better or worse off had they
chosen a flat-fee broker because there are fewer sellers with the same characteristics
who used a flat-fee broker.
6 Discussion
Houses sold with flat-fee agents seem to exhibit a consistent pattern: (i) they sell
at a higher transaction price and (ii) they sell faster (at least conditional on sale).
Even though there is a weak evidence that flat-fee houses are somewhat less likely to
sell, we also find that it cannot explain the other predictions. Proximity, experience,
and size of the broker have no effect on the realized transaction price, and only a
negligible effect on the time to sale. Below, we discuss several potential explanations
for our results: (i) market power, (ii) asymmetric information, and (iii) broker
6.1 Market power of brokers
The fact that a flat fee leads to better sales performance suggests that the market for
traditional real-estate agents is not competitive. We are hardly the first to point this
out; see for example, Levitt and Syverson (2008a) and Bernheim and Meer (2013).
Even in the financial industry there is clear evidence of persistent inefficiency in the
allocation of retail investor funds to mutual funds; see Del Guercio et al. (2010).
The explanation of limited financial literacy by clients is potentially magnified in the
housing market, which consists of high-stakes transactions that participants engage
in only a few times in their lifetime.
6.2 Mitigation of Asymmetric Information
Under the flat-fee structure, sellers are responsible for the viewings themselves. The
personal interaction of hosting the viewings could convey information that decreases
the asymmetric information problem. The face-to-face interaction between buyer
and seller, and the factual knowledge about the house and the neighborhood may
induce the buyer to pay more for the house than when faced with a broker. Lewis
(2011) finds that the market for cars on eBay functions, largely because sellers give
concrete and verifiable information about the particular car they are selling. In the
housing market, viewings hosted by the seller could have the same effect: the seller
can provide details on maintenance and neighborhood characteristics that a typical
real-estate agent would find much harder to provide. Note that this explanation is
consistent with a non-competitive real-estate market.
6.3 Broker specialization
The better performance of flat-fee brokers could reflect a more efficient division of
labor: flat-fee brokers specialize in the skills that are most relevant for transaction
performance, such as price setting, salesmanship and negotiation. The seller takes
on the labor-intensive, but low-skilled, work of hosting viewings, generally providing
better information about the house and displaying some salesmanship. In addition,
the variable costs of rejecting an offer are lower than for full-service brokers, as
the costs of organizing and conducting the viewings are borne by the seller and
not the agent. The lower opportunity costs of rejecting an offer could explain why
transaction prices are higher using a flat fee.
Note that the last two explanations imply a potential efficiency gain that
remains largely unexploited, given the low market share of flat-fee brokers – the
persistence of which could be explained by limited attention, limited rationality,
inexperience of sellers or a combination of these factors. The existence of persistent
inefficiency in a competitive market is not impossible, as documented by Cho and
Rust (2010), who give evidence for this in the rental car market.
7 Final remarks
After an online centralized listing service for real estate was introduced in 2001, it
became profitable for flat-fee brokers to enter the Dutch market. Brokers charge the
flat fee up-front and delegate the viewings to the sellers. Flat-fee brokers are thus a
low-cost alternative to traditional full-service brokers. Our analysis provides a strong
statistical evidence that the performance of flat-fee brokers is better: they obtain
higher sales prices, at lower sales times. Consequently, the profits of traditional
full-service brokers partly reflect rents.
The existence of a large rent-seeking component in the compensation of tradi-
tional brokers is consistent with limited price competition. The strong performances
of flat-fee brokers has not (yet) eliminated traditional brokers, possibly due to lim-
ited attention and inexperience of sellers in the housing market.
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A Estimator for the average treatment effect among
the treated
We have that
P(Pit w|Xit =x) = ZI(x)|Zit=1
1(P(x, i)w)dFIit|Zit =z,Xit (i|Zit =z, Xit =x)
Hence, we can estimate ∆T T using
T T =1
1(Zit = 1) 1
Pit(Xit )X
M T E (Xit, w)1b
Pit(Xit , Iit)w(5)
where NZit=1 = #{Zit = 1, i = 1, . . . , N , t = 1,...Ti}. To estimate P(x) = P(Zit =
1|Xit =x), we could average over the estimated values of P(x, i). However, since x
is continuous and highly dimensional, this is not an attractive approach. Therefore, we
estimate P(x) as a linear probability model of Zit on Xit instead. We propose the following
four-step procedure
1. Estimate both P(Xit, Iit) and P(Iit ) by using a linear probability model of Zit on
either Xit and Iit or only Xit.
2. Estimate a series regression of Yit on Xit and P(Xit, Iit ) to estimate E(Yit|Xit =
x, P (Xit, Iit) = p).
3. Estimate ∆M T E (Xit) as the first order derivative of the regression of the second
4. Estimate ∆T T using (5).
ResearchGate has not been able to resolve any citations for this publication.
Within the Oregon University System’s defined contribution retirement plan, one investment provider offers access to face-to-face financial advice through its network of brokers. We find that younger, less highly educated, and less highly paid employees are more likely to choose this provider. To benchmark the portfolios of broker clients, we use the actual portfolios of self-directed investors and counterfactual portfolios constructed using target-date funds, a popular default investment. Broker clients allocate contributions across a larger number of investments than self-directed investors, and they are less likely to remain fully invested in the default option. However, broker clients’ portfolios are significantly riskier than self-directed investors’ portfolios, and they underperform both benchmarks. Exploiting across-fund variation in broker compensation, we find that broker clients’ allocations are higher when broker fees are higher. Survey responses from current plan participants support our identifying assumption that the portfolio choices of broker clients reflect the recommendations of their brokers.
I propose a model of the housing market using a search framework with asymmetric information in which sellers are unable to commit to asking prices announced ex ante. Relaxing the commitment assumption prevents sellers from using price posting as a signalling device to direct buyers' search. Adverse selection and inefficient entry on the demand side then contribute to housing market illiquidity. Real estate agents that can improve the expected quality of a match can segment the market and alleviate information frictions. Even if one endorses the view that real estate agents provide no technological advantage in the matching process, incentive compatible listing contracts are implementable as long as housing is not already sufficiently liquid. The theoretical implications are qualitatively consistent with the empirical observations of real estate brokerage: platform differentiation, endogenous sorting, and listing contract features that reinforce incentive compatibility.
In stark contrast to other agency-based consumer service industries, real estate agents have been remarkably effective at maintaining margins and market share. One possible explanation for the success of real estate agents is collusion, and consequently the industry is facing regulator scrutiny. In this paper we first consider the mechanisms through which collusion might be sustained. Although the industry's low concentration makes collusion harder to support, the involvement of both a seller's agent and a buyer's agent on a given home sale helps facilitate collusion. Selling agents need buyer's agents to deliver customers. Not only can full-commission agents collude against discount/flat-fee agents by steering their buyers away from such listings, they also can use the same punishment on other full-commission agents who cooperate with discount agents. Using data from three markets, we find that houses listed using flat-fee agents have longer expected times-to-sale than observably similar houses sold by full-commission agents, but ultimately sell for similar prices. These results are consistent with allegations that traditional agents steer clients away from flat-fee-listed homes, although we consider other possible explanations. We calculate that, even taking into account the longer expected time to sale and the increased effort of the homeowner, sellers who use flat-fee agents saved an average of over $5,000 compared to hiring a full-commission agent. This casts doubt on the necessity of minimum-service laws that exist in several states and are being considered in others.
Transaction costs are thought to affect asset prices and market liquidity, but the direction and magnitude of these effects continues to be the subject of debate. In the single family residential market discount brokers offer to list a house for a lower price and thus reduce the transaction costs associated with obtaining a match. In this paper we obtain empirical estimates of the price and liquidity impact of a seller selecting a discount broker to market a single family residential property. The unique data set allows for the identification of residential properties that were listed by a discount brokerage firm. The empirical results confirm the predictions of our theoretical model. Using a sample of 318,211 listings and 243,625 sales, we find that houses listed by discount brokers sell at prices similar to non-discount brokerage listings, but are less likely to sell and when they do sell, take approximately 3 days longer to sell. The results indicate that lower transaction costs do not impact housing prices in this market, but are related to asset liquidity.
This paper measures the effects of real estate brokerage services provided to sellers, other than MLS listings, on the terms and timing of home sales. It is not obvious that sellers benefit from those services. On the one hand, brokers offer potentially useful knowledge and expertise. On the other hand, because the relationship between the homeowner and the broker resembles a classical principal-agent problem, the broker may not deploy services in ways that promote the seller’s interests. Yet as long as valuable MLS listings are bundled with brokerage services, homeowners may use brokers even if the agency costs exceed the benefits of brokers’ knowledge and expertise. Thus, quantification of the net value of brokerage services other than MLS listings bears directly on the recent policy debate over the desirability unbundling of MLS listings. We estimate the effect of a seller’s decision to use a broker on list prices, selling prices, and speed of sale for a real estate market with an unusual and critical characteristic: it has a single open-access listing service that is used by essentially all sellers, regardless of whether they employ brokers. Our central finding is that, when listings are not tied to brokerage services, a seller’s use of a broker reduces the selling price of the typical home by 5.9 to 7.7 percent, which indicates that agency costs exceed the advantages of brokers’ knowledge and expertise by a wide margin.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at
This article provides an empirical framework to study entry and cost inefficiency in the real estate brokerage industry. We develop a structural entry model that exploits individual level data on entry and earnings to estimate potential real estate agents' revenues and reservation wages, thereby recovering costs of providing brokerage service. We estimate the model, using the Census data. Based on our cost estimates, we find strong evidence for cost inefficiency under free entry, particularly attributable to wasteful non-price competition. We further use the estimated model to evaluate welfare implications of the rebate bans that currently persist in some U.S. states. We find that removing rebate bans would decrease the equilibrium number of real estate agents by 5.14% and reduce total brokerage costs by 8.87%.
When a homeowner uses an agent to sell his property, he may have less information than his agent and be disadvantaged in price setting and negotiating. This study examines whether the percentage commission structure in real estate brokerage creates agency problems. We investigate whether agents are able to use their information advantage to either sell their own property faster or for a higher price than their clients’ properties. The empirical results confirm our theoretical predictions of agency problems, as we find that agent-owned houses sell no faster than client-owned houses, but they do sell at a price premium of approximately 4.5%.
Economics Since Akerlof (1970), economists have understood the adverse selection problem that information asymmetries can create in used goods markets. The remarkable growth in online auctions of used goods, where buyers generally purchase sight unseen, therefore poses a puzzle. I argue that part of the solution is that sellers voluntarily disclose their private information to buyers through photos, text and graphics on the auction webpage. In so doing they define a precise contract between buyer and seller — to deliver the car shown — and this helps protect the buyer from adverse selection. Extending previous theoretical work by Jovanovic (1982), I model the impact of contractible disclosure and changes in disclosure costs on performance and adverse selection on online auction platforms. To test this theory, I examine data from eBay Motors. I find first that sellers selectively disclose information; second that this reduces information asymmetry; and finally that disclosure costs impact both the level of disclosure and the prices obtained by sellers, and consequently incentives for seller participation.
Economics Many consumers make poor financial choices and older adults are particularly vulnerable to such errors. About half of the population between ages 80 and 89 either has dementia or a medical diagnosis of “cognitive impairment without dementia.” We study lifecycle patterns in financial mistakes using a proprietary database that measures ten different types of credit behavior. Financial mistakes include suboptimal use of credit card balance transfer offers, misestimation of the value of one’s house, and excess interest rate and fee payments. In a cross-section of prime borrowers, middle-aged adults make fewer financial mistakes than younger and older adults. We conclude that financial mistakes follow a U-shaped pattern, with the cost-minimizing performance occurring around age 53. We analyze regulatory regimes that may help individuals avoid making financial mistakes. Some of these regimes are designed to address the particular challenges faced by older adults, but much of our discussion is relevant for all vulnerable populations. We discuss disclosure, nudges, financial driving licenses, advanced directives, fiduciaries, asset safe harbors, ex-post and ex-ante regulatory oversight. Finally, we pose seven questions for future research on cognitive limitations and associated policy responses.